Computing Optimal Joint Chance Constrained Control Policies

We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard dynamic programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, a...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 70; no. 7; pp. 4904 - 4911
Main Authors Schmid, Niklas, Fochesato, Marta, Li, Sarah H.Q., Sutter, Tobias, Lygeros, John
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Aerospace electronics
Constraints
Costs
Dynamic programming
Dynamic programming (DP)
Heuristic algorithms
joint chance constrained programming
Kernel
Optimal control
Optimization
Policies
Programming
Safety
stochastic optimal control
Stochastic processes
Trajectory
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Summary:We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard dynamic programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably optimal and easy-to-compute policies are still missing. We fill this gap by augmenting the dynamics via a binary state, allowing us to characterize the optimal policies and develop a dynamic programming-based solution method.
Bibliography:ObjectType-Article-1
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content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2025.3546078